Well-to-well log correlation is a task often performed by geoscientists looking for consistency or change between patterns or signatures in well log data measured from sub-surface geological formations. A well log may be visually displayed as a curve representing the measurement of a rock property plotted against well depth. Since well log curve amplitude measurements generally vary depending on the type of rock and the amount of water, oil or gas within rocks, well logs are typically used to identify patterns or signatures indicative of naturally occurring compounds, such as hydrocarbons, at various depths in a well. Once a signature of interest (i.e., a continuous depth varying amplitude pattern) in a well log curve from a well has been identified, the signature, or a very similar depth varying amplitude pattern is then searched for in logs from nearby wells. Well-to-well log correlation is often useful in the petroleum industry where knowledge of the earth's sub-surface is necessary to efficiently find and extract oil reserves.
Historically, well log correlation was manually performed by an interpreter who visually compared paper “reference” well logs and “target” well logs to look for a particular signature (or similar variation thereof). This manual process was, in many cases, a time intensive task as the amplitude of the curves representing the target well logs would need to be visually stretched or squeezed for the interpreter to “find” the reference signature in the target well logs. In addition, if a large number of wells were being correlated, the task also became manpower intensive, as it required the visual inspection of each well log for the desired signature.
More recently, interactive computer implemented solutions have been developed which require the user to divide the signature from the reference well into intervals, optionally applying uniform stretching and squeezing of that interval, and use a workstation mouse to move each interval along the target well's track to find a good fit. These solutions, however, are potentially time intensive, and often offer little improvement over previous manual methods using paper logs.
In an effort to reduce the manpower and time traditionally required to correlate well log data, several alternative computer implemented solutions have been proposed to automate this task. However, these alternative solutions, which have included expert systems, neural networks, dynamic programming, combinations of expert systems and statistics, and combinations of expert systems and dynamic programming, have all been previously tried without success.